Self-adaptive salp swarm algorithm for optimization problems

In this paper, an enhanced version of the salp swarm algorithm (SSA) for global optimization problems was developed. Two improvements have been proposed: (i) Diversification of the SSA population referred as SSAstd\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{std}$$\end{document}, (ii) SSA parameters are tuned using a self-adaptive technique-based genetic algorithm (GA) referred as SSAGA-tuner\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{GA-tuner}$$\end{document}. The novelty of developing a self-adaptive SSA is to enhance its performance through balancing search exploration and exploitation. The enhanced SSA versions are evaluated using twelve benchmark functions. The diversified population of SSAstd\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{std}$$\end{document} enhances convergence behavior, and self-adaptive parameter tuning of SSAGA-tuner\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$_{GA-tuner}$$\end{document} improves the convergence behavior as well, thus improving performance. The comparative evaluation against nine well-established methods shows the superiority of the proposed SSA versions. The enhancement amount in accuracy was between 2.97 and 99% among all versions of algorithm. In a nutshell, the proposed SSA version shows a powerful enhancement that can be applied to a wide range of optimization problems.